Other related work includes [1]} who introduced a dataset containing 32k instances of real-world policies, crowd-sourced fictional life scenarios, and dialogues in order to reach a final yes/no answer. The policies were given as input and were explicitly stating what information needed to be asked for, and the questions had to have a yes or no answer. Neither of these hold in fact-checking, where questions are not usually answered by yes or no, and the information to be searched for is not known in advance.
More recently, [2]} presented a method to generate clarification questions.
They built a two-stage framework that identifies missing information using the notions of global and local schemas. The global schema was built using filtered key phrases extracted from contexts that were part of the same class of the data, e.g. a class of similar products in the Amazon data [3]} and similar dialogues in the Ubuntu dataset [4]}, whereas the local schema was built using one given context, and they defined the missing information as the difference between the global and the local schema. The extraction of comparable schemas across different contexts was possible due to the repetitive nature of the datasets considered, e.g. the descriptions of products of the same type such as laptops allow the prediction of potentially missing properties which need clarification, in contrast to fact-checking claims which are less repetitive.
